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165 results for “baboon”
Guinea baboon vocalizations dataset automatically extracted with a deep neural network from natural audio recordings
<p><strong>Abstract</strong></p> <p>The data collection process consisted of continuously recording during one month a group of Guinea baboons living in semi-liberty at the CNRS primatology center in Rousset-sur-Arc (France). Two microphones we placed nearby their enclosure to continuously record the sounds produced by the group. A convolutional neural network (CNN) was used on these large and noisy audio recordings to automatically extract segments of sound containing a baboon vocal production by following the method of <a href="https://arxiv.org/abs/2302.07640">Bonafos et al. (2023)</a>. The resulting dataset consists of one-second to several-minute wav files of automatically detected vocalizations segments. The dataset thus provides a wide range of baboon vocalizations produced at all times of the day. It can be used to study vocal productions of non-human primates, their repertoire, their distribution over the day, their frequency, and their heterogeneity. In addition to the analysis of animal communication, the dataset can also be used as a learning base for sound classification models.</p> <p> </p> <p><strong>Data acquisition</strong></p> <p>The data are audio recordings of baboons. The recordings were made with a H6 Zoom recorder, using the included XYH-6 stereo microphone. The sample size is 44100 Hertz, 16 bits. The microphones were placed in the vicinity of the enclosure for one month and recorded continuously on a PC computer. A CNN passed over the data with a sliding window of 1 second and an overlap of 80% to detect the vocal productions of the baboons. The dataset consists of the segments predicted by the CNN to contain a baboon vocalization. Windows containing signal less than one second apart were merged into a single vocalization.</p> <p> </p> <p><strong>Data source location</strong></p> <ul> <li>Institution: CNRS, Primate Facility</li> <li> <p>City/Town/Region: Rousset-sur-Arc</p> </li> <li> <p>Country: France</p> </li> <li> <p>Latitude and longitude for collected samples/data: 43.47033535251509, 5.6514732876668905</p> </li> </ul> <p> </p> <p><strong>Value of the data</strong></p> <ul> <li> <p>This dataset is relatively unique in terms of the quantity of vocalizations available.</p> </li> <li> <p>This massive dataset can be very useful to two types of scientific communities: experts in primatology who study the vocal productions of non-human primates, and experts in data science and audio signal processing.</p> </li> <li> <p>The machine learning research community has at its disposal a database of several dozen hours of animal vocalizations, which will make it possible to build up a large learning base, very useful for Environemental Sound Recognition tasks, for example.</p> </li> </ul> <p> </p> <p><strong>Objective</strong></p> <p>This dataset is a follow-up of two studies on the vocal productions of Guinea baboons (Papio papio) in which we carried out analyses of their vocal productions on the basis of a relatively large vocalization sample containing around 1300 vocalizations (<a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0169321">Boë, Berthommier, Legou, Captier, Kemp, Sawallis, Becker, Rey, & Fagot, 2017</a>; <a href="https://hal.science/hal-01649539">Kemp, Rey, Legou, Boë, Berthommier, Becker, & Fagot, 2017</a>). The aim was to collect a larger database using the technique of deep convolutional neural networks in order to 1) automatically detect vocal productions in a large continuous audio recording and 2) perform a categorization of these vocalizations on a more massive sample. A description of the pipeline that enabled these automatic detections and categorizations is given in <a href="https://arxiv.org/abs/2302.07640">Bonafos, Pudlo, Freyermuth, Legou, Fagot, Tronçon, & Rey (2023)</a>.</p> <p> </p> <p><strong>Data description</strong></p> <p>The data is a set of audio files in wav format. They are at least one second long (the size of the window), up to several minutes, if several windows are consecutively predicted as containing signal. Moreover, we add the labeled data we used to train the CNN which did the prediction. We also provide two hours of the continuous recordings to have an idea of the continuous recordings and test the code of the paper provided on <a href="https://gitlab.com/papers4375727/detection-and-classification-of-vocal-productions">gitlab</a>.</p> <p>In addition, there is a database in csv format listing all the vocalizations, the day and time of their production, and the prediction probabilities of the model.</p> <p> </p> <p><strong>Experimental design, materials and methods</strong></p> <p>The original recordings represent one month of continuous audio recording. Seven hours of this month were manually labelled. They were segmented and labelled according to whether or not there was a monkey vocalization (i.e., noise or vocalization) and, if there was a vocalization, according to the type of vocalization (6 possible classes: bark, copulation grunt, grunt, scream, yak, wahoo). These manually labelled data were used as a training set for a CNN, which was automatically trained following the pipeline of Bonafos et al. (2023). This model was then used to automatically detect and classify vocalization during the whole month of audio recording. It processes the data in the same way when predicting new data as it does when training. It uses a sliding window of one second with an overlap of 80%. It does not take into account information from previous predictions, but calculates the probability of a vocalization in each one-second window independently. It then iterates through the month. For each window, the model predicts two outputs: the probability that there is a vocalization and the probability of each class of vocalization.</p> <p>For the purpose of generating the wav files, if a window has a probability of a vocalization greater than 0.5, it is considered to contain a vocalization. If it is the first one, a vocalization is started at that moment. If the time windows that follow a vocalization also contain a vocalization, then the signal they contain is added to the first segment for which a vocalization has been detected. As soon as a one-second segment no longer contains a signal corresponding to a vocalization, the wav file is closed. If windows are predicted to contain no vocalizations, but are between two windows that contain vocalizations within 1 second of each other, then all windows are merged.</p>
Chacma baboon male-male greeting dataset, Gorongosa National Park, Mozambique
<p><em><strong>See README file for more complete information</strong></em></p> <p><strong>Overview: </strong>This dataset provides presence/absence data for each of 55 observations from Gorongosa National Park. Each observation is one male-male greeting. Data was first collected using BORIS (Friard and Gamba, 2016) following an ethogram (DOI:10.5281/zenodo.7314291), then cleaned using Python version 3.8.5 and R version 4.3.1 to format as a presence/absence matrix. Some of these observations (with observation IDs ending in "_greet[xx]" also appear in the more general approach dataset found under the following DOI 10.5281/zenodo.10339023. These observations are part of both datasets as they qualify both as male-male greetings and also as approaches from outside 5 m to within 2 m. </p> <p><strong>Study species: </strong><em>Papio ursinus griseipes</em> (chacma baboon)</p> <p><strong>Dates of data collection:</strong> Videos in Gorongosa National Park were filmed 2018-10 to 2018-11 and 2019-07 to 2019-11. </p> <p><strong>Geographic location of data collection:</strong> Gorongosa National Park, Mozambique<br><br><strong>Video coding software: </strong>BORIS (Friard and Gamba, 2016)</p> <p><strong>Recommended citation in BibTex form:</strong></p> <p>@ELECTRONIC{Muschinski2024,<br> author = {Muschinski, Jana and Carvalho, Susana},<br> year = {2024},<br> title = {Chacma baboon male-male greeting dataset, {G}orongosa {N}ational {P}ark, {M}ozambique},<br> doi = {10.5281/zenodo.11097938},<br> owner = {Paleo-Primate Project, Gorongosa National Park},<br> organization = {Paleo-Primate Project, Gorongosa National Park}<br>}</p>
Baboon approach behaviour data - Gorongosa National Park, Mozambique and Gombe National Park, Tanzania
<p><em><strong>See README file for more complete information</strong></em></p> <p><strong>Overview: </strong>This dataset provides presence/absence data for each of 299 observations from Gorongosa National Park and 73 observations from Gombe National Park. Each observation is one "proximity event", where an adult/subadult baboon approached another adult/subadult baboon from outside 5 m to within 2 m. Data was first collected using BORIS (Friard and Gamba, 2016) following an ethogram (DOI:10.5281/zenodo.7314291), then cleaned using Python version 3.8.5 and R version 4.0.5 to format as a presence/absence matrix. </p> <p><strong>Study species: </strong>Papio ursinus griseipes (chacma baboon) and Papio anubis (olive baboon) where specified</p> <p><strong>Dates of data collection:</strong> Videos in Gorongosa National Park were filmed 2018-10 to 2018-11 and 2019-07 to 2019-11. Videos in Gombe Stream national Park were recorded 2021-1 to 2021-5 and 2021-8 to 2021-9.</p> <p><strong>Geographic location of data collection:</strong> Gorongosa National Park, Mozambique and Gombe Stream National Park, Tanzania as specified by observation<br><br><strong>Video coding software: </strong>BORIS (Friard and Gamba, 2016)</p> <p><strong>Recommended citation in BibTex form:</strong></p> <p>@ELECTRONIC{Muschinski2022b,<br> author = {Muschinski, Jana and Carvalho, Susana},<br> year = {2023},<br> title = {Baboon approach behaviour data - {Gorongosa National Park}, {Mozambique} and {Gombe National Park}, {Tanzania}},<br> doi = {10.5281/zenodo.10339023},<br> owner = {Paleo-Primate Project, Gorongosa National Park},<br> organization = {Paleo-Primate Project, Gorongosa National Park}<br>}<br> </p>
Baboon and Gelada SNP Calls VCF
<p>Bgzipped vcf and tabix index files of baboon and gelada SNP calls of 16 individuals on the papAnu2 assembly as published in Rogers et al. (2019). The comparative genomics and complex population history of Papio baboons. Science Advances. 30 Jan 2019: Vol. 5, no. 1, eaau6947 DOI: 10.1126/sciadv.aau6947.</p>
Fig. 1 in Severe coenurosis caused by larvae of Taenia serialis in an olive baboon (Papio anubis) in Benin
Fig. 1. Clinical presentation of the baboon with the clear presence of swellings in various areas of the body: ventral abdominal and thoracic parts, inner part of forearms, intermandibular region (arrow heads) and dorsal region also.
Fig. 4 in Predictors of helminth parasite infection in female chacma baboons (Papio ursinus)
Fig. 4. An interaction between host reproductive state and progestagen concentrations in the infection intensity of Protospirura. Pregnant females (blue) exhibit increased infection intensity of Protospirura with rising progestagen concentrations. Non-pregnant females (red) exhibit decreased infection intensity of Protospirura with rising progestagen concentrations. Confidence intervals are in gray. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in Predictors of helminth parasite infection in female chacma baboons (Papio ursinus)
Fig. 2. Plots showing associations between log Protospirura intensity (eggs per gram; epg) in female baboons and marginal effects of each predictor variable. Plots are (A) pregnant (no or yes); (B) log progestagen concentrations (low = below median; high = above median; ng/g); (C) season (dry or wet); (D) presence/absence of Oesophagostomum; and (E) log Trichuris intensity (epg). Points and whiskers on the plot represent the mean and confidence intervals. For Fig. 2E, the values of each fixed effect are divided into tertiles. Numbers above each bar indicate sample size.
Fig. 3 in Predictors of helminth parasite infection in female chacma baboons (Papio ursinus)
Fig. 3. Plots showing associations between log Trichuris intensity (eggs per gram; epg) in female baboons and marginal effects of each predictor variable. Plots are (A) cycling (no or yes); (B) log fecal glucocorticoid concentrations (ng/g); (C) season (dry or wet); (D) presence/absence of Oesophagostomum; and (E) log Protospirua intensity (epg). Points and whiskers on the plot represent the mean and confidence intervals. For Fig. 3B and E, the values of each fixed effect are divided into tertiles. Numbers above each bar indicate sample size. Photograph by Bobby Habig.
Fig. 1 in Predictors of helminth parasite infection in female chacma baboons (Papio ursinus)
Fig. 1. Population and host level processes proposed to drive within group variation in helminth infection risk among female chacma baboon hosts (partially adapted from Akinyi et al., 2019; Habig et al., 2019). Four key drivers of parasite risk are examined: environmental conditions; reproductive stage; steroid hormones; and patterns of coinfection.
Data for: Five decades of data yield no support for adaptive biasing of offspring sex ratio in wild baboons (Papio cynocephalus)
<p>Over the past 50 years, a wealth of testable, often conflicting, hypotheses has been generated about the evolution of offspring sex ratio manipulation by mothers. Several of these hypotheses have received support in studies of invertebrates and some vertebrate taxa. However, their success in explaining sex ratios in mammalian taxa, and especially in primates, has been mixed. Here, we assess the predictions of four different hypotheses about the evolution of biased offspring sex ratios in the well-studied baboons of the Amboseli basin in Kenya: the Trivers-Willard, female rank enhancement, local resource competition, and local resource enhancement hypotheses. Using the largest sample size ever analyzed in a primate population (n = 1372 offspring), we test the predictions of each hypothesis. Overall, we find no support for adaptive biasing of sex ratios. Offspring sex is not consistently related to maternal dominance rank or biased towards the dispersing sex, nor it is predicted by group size, population growth rates, or their interaction with maternal rank. Because our sample size confers power to detect even subtle biases in sex ratio, including modulation by environmental heterogeneity, these results suggest that adaptive biasing of offspring sex does not occur in this population.</p>
Social network inheritance and differentiation in wild baboons
<p>Immatures' social development may be fundamental to understanding important biological processes, such as social information transmission through groups, that can vary with age and sex. Our aim was to determine how social networks change with age and differ between sexes in wild immature baboons, group-living primates that readily learn socially. Our results show that immature baboons inherited their mothers' networks and differentiated from them as they aged, increasing their association with partners of similar age and the same sex. Males were less bonded to their matriline and became more peripheral with age compared to females. Our results may pave the way to further studies testing a new hypothetical framework: in female-philopatric societies, social information transmission may be constrained at the matrilineal level by age- and sex-driven social clustering.</p>
Data for: Five decades of data yield no support for adaptive biasing of offspring sex ratio in wild baboons (Papio cynocephalus)
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Data from: Social and environmental predictors of gut microbiome age in wild baboons
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Social network inheritance and differentiation in wild baboons
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Data from: Demographic, behavioral, and ecological data from a long-term field study of wild baboons in Amboseli, Kenya
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Data from: Multi-scale predictors of parasite risk in wild male savanna baboons (Papio cynocephalus)
Several factors are thought to shape male parasite risk in polygynous and polygynandrous mammals, including male-male competition, investment in potentially immunosuppressive hormones, and dispersal. Parasitism is also driven by processes occurring at larger scales, including host social groups and populations. To date, studies that test parasite-related costs of male behavior at all three scales—individual hosts, social groups, and the host population—remain rare. To fill this gap, we investigated multi-scale predictors of helminth parasitism in 97 male savanna baboons (Papio cynocephalus) living in the Amboseli ecosystem in Kenya over a five-year span. Controlling for multi-scale processes, we found that many of the classic indicators of male mating effort—high dominance rank, testosterone, and glucocorticoids—did not predict helminth infection risk. However, we identified two parasite-related costs associated with male behavior: (i) socially connected males exhibited higher Trichuris trichiura egg counts and greater parasite species richness than socially isolated males; and (ii) males with stable group residency exhibited higher parasite species richness than males who frequently dispersed to new social groups. At the population level, males harbored more parasites following periods of drought than rainfall. Lastly, parasites exhibited positive covariance suggesting that infection risk increases if a host already harbors one or more parasite taxa. These results indicate that multi-scale processes are important in driving male parasite risk, and that some aspects of male behavior are costly. Together, our results provide an unusually holistic perspective on the drivers of parasite risk in the context of male behaviors and life histories.
Data from: Estimation of energetic condition in wild baboons using fecal thyroid hormone determination
Understanding how environmental and social factors affect reproduction through variation in energetic condition remains understudied in wild animals, in large part because accurately and repeatedly measuring energetic condition in the wild is a challenge. Thyroid hormones (THs), such as triiodothyronine (T3) and thyroxine (T4), have a key role in mitigating metabolic responses to energy intake and expenditure, and therefore are considered important biomarkers of an animal's energetic condition. Recent method development has shown that T3 and T4 metabolites can be measured in feces, but studies measuring THs in wild populations remain rare. Here we measured fecal T3 metabolites (mT3) in baboons, and tested whether the conditions of collection and storage used for steroid hormones could also be used for mT3; we focused on mT3 as it is the biologically active form of TH and because fecal T4 metabolites (mT4) were below detection levels in our samples. We also tested if mT3 could be determined in freeze-dried samples stored for long periods of time, and if these concentrations reflected expected biological variations across seasons and reproductive states. Our results show that mT3 can be measured with accuracy and precision in baboon feces. The conditions of collection and storage we use for steroid hormones are appropriate for mT3 determination. In addition, mT3 concentrations can be determined in samples stored at -20 °C for up to 9 years, and are not predicted by the amount of time in storage. As expected, wild female baboons have lower mT3 concentrations during the dry season. Interestingly, mT3 concentrations are lower in pregnant and lactating females, possibly reflecting an energy sparing mechanism. Retroactive determination of mT3 concentration in stored, freeze-dried feces opens the door to novel studies on the role of energetic condition on fitness in wild animals.
Data from: Ovarian cycling and reproductive state shape the vaginal microbiota in wild baboons
Background: The vaginal microbiome is an important site of bacterial-mammalian symbiosis. This symbiosis is currently best characterized for humans, where lactobacilli dominate the microbial community and may help defend women against infectious disease. However, lactobacilli do not dominate the vaginal microbiota of any other mammal studied to date, raising key questions about the forces that shape the vaginal microbiome in non-human mammals. Results: We used Illumina sequencing of the bacterial 16S rRNA gene to investigate variation in the taxonomic composition of the vaginal microbiota in 48 baboons (Papio cynocephalus), members of a well-studied wild population in Kenya. Similar to prior studies, we found that the baboon vaginal microbiota was not dominated by lactobacilli. Despite this difference, and similar to humans, reproductive state was the dominant predictor of baboon vaginal microbiota, with pregnancy, postpartum amenorrhea, and ovarian cycling explaining 18% of the variance in community composition. Furthermore, among cycling females, a striking 39% of variance in community composition was explained by ovarian cycle phase, with an especially distinctive microbial community around ovulation. Peri-ovulatory females exhibited the highest relative abundance of lactic acid-producing bacteria compared to any other phase, with a mean relative abundance of 44%. To a lesser extent, sexual behavior, especially a history of shared sexual partners, also predicted vaginal microbial similarity between baboons. Conclusions: Despite striking differences in their dominant microbes, both human and baboon vaginal microbiota exhibit profound changes in composition in response to reproductive state, ovarian cycle phase, and sexual behavior. We found major shifts in composition during ovulation, which may have implications for disease risk and conception success. These findings highlight the need for future studies to account for fine-scale differences in reproductive state, particularly differences between the various phases of the ovarian cycle. Overall, our work contributes to an emerging understanding of the forces that explain intra- and inter-individual variation in the mammalian vaginal microbiome, with particular emphasis on its role in host health and disease risk.
Thoth as baboon
The small statue of turquoise faience (inventory no. 13) is approx. 4.5 cm high and dates to the Late Period (approx. 664–332 B.C.). It shows the god Thoth as baboon in a squatting positon holding a Wedjat-eye—a symbol of protection—in its hands above its knees. A hook is situated at the back suggesting the object was worn as an amulet. Thoth is associated with different aspects of Egyptian culture as a god of nature, cosmology, writing, science and medicine. Thoth's sacred animals are the ibis and the baboon. Source: Objaverse 1.0 / Sketchfab
Magnetic Resonance Imaging Scan of a Baboon Brain (Papio Papio)
<p>Magnetic resonance imaging scan of a baboon brain (Papio papio) from http://braincatalogue.org/Baboon</p>
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